Current state regarding seasonal to interanual climate prediction

160 L.A. Ogallo et al. Agricultural and Forest Meteorology 103 2000 159–166 influences the space-time distribution of most agricul- tural systems. Such agricultural systems will therefore be vulnerable to interannual climate variability, espe- cially the extreme events such as excessive precipita- tion, drought, cyclones, hotcold spells etc., together with any abrupt changes in the traditional patterns of the local, regional and global climate. The extreme climate events also often adversely affect land use planning, level of agricultural yield, consistency in yield, cost of production, sowing and harvesting, irrigation needs, transportation, storage, pests and diseases, marketing, farm management, food security and many other socio-economic indicators. They are further known to cause massive loss of life and damage to property, including agricultural invest- ments. Vulnerability of the agricultural systems to in- terannual climate variability is often higher in many developing countries where most of the agricultural systems are precipitation dependent with very low technical adaptations. Where greater responsibility is placed on farmers for environmental management they must increas- ingly rely on climateforecast information and climate predictions for operational and strategic decisions and planning affecting environmental, agronomic and economic sustainability. This review has been divided into three major sections in order to adequately address the theme of the paper. The first part will present known linkages between climate and agricultural systems, while part two will review the current state of climate prediction science. The last part presents some key issues which must be addressed in order to enable agricultural planning and operations to take maximum advantage of the current climate prediction methods, together with those which are expected to emerge in the next century.

2. Linkages between climate and agricultural resources

As has been highlighted in the last section, cli- mate has both direct and indirect impacts on many agricultural activities, including adaptation and land use practices, soil and vegetation conditions, nutri- ent loss, water availability, physiological conditions, environmental degradation such as environmental pollution and desertification, pests and diseases, transportation, marketing and many other agricultural indicators which are crucial to sustainable national development. The impacts of interannual climate variability, especially the extreme events, are often very dev- astating, especially in developing countries where technological adaptations are still minimal. In devel- oped countries, the changing role and characteristics of farming, which include improved quality and de- creased use of chemicals has also emphasised the needs of farmers for information on practices based on current and forecast weather and longer-term climate predictions. Advance warning of hazards and extreme climate anomalies at time scales of months to years would therefore be extremely important in agricultural planning and operations. Apart from the traditional weather information, agricultural systems would benefit from advance information regarding future expectations of the following, among many others: • For scales of less than a month, dailyweekly10-day precipitation amount, intensity, number of rainy days, distribution of the wetdry spells; tempera- ture information on heat waves, cold spells and frosts. • Seasonal climate information, such as time of onset of rainfall or drought, or any extreme climate event, its duration and extent. • On an inter-annual time scale, the probability of per- sistence of an extreme, the probability of switching to another state, e.g. from drought to flood, within the period of the forecast. • Climate change resulting into significant shifts of the traditional patterns of weather and climate. The next section will address the current state of sea- sonal prediction science and technology which could be used to provide relevant advance climate informa- tion for agricultural planning and operation.

3. Current state regarding seasonal to interanual climate prediction

It was noted in Section 2 that advance warning of future expectations of extreme weatherclimate events is extremely useful in agricultural planning and oper- ations. In general, climate information and prediction L.A. Ogallo et al. Agricultural and Forest Meteorology 103 2000 159–166 161 products required in agricultural applications can be classified into the following categories: • Historical and past climate records; • Real-time and near real-time information; • Short-range weather forecasts of up to about 1 week; • Medium-range weather forecasts of time scales beyond the short-range and up to about 2 weeks; • Long-range weather forecasts and climate predic- tion of monthly, seasonal and interannual time scales; • Climate change information. This section is expected to highlight the major recent advancements regarding monthly, seasonal and in- terannual climate predictions. It would, however, be futile to address issues related to climate prediction science and technology without a brief discussion of the other time scales since they are directly or indirectly, fundamental to climate prediction. 3.1. Historical and past climate records All relationships between climate and agricultural systems are derived from historical and past records of both climate and agriculture. Such records are also used in deriving the basic statistics and risks that may be associated with any climate based planning and operational decisions. Availability of long period, high quality climate and agricultural records are therefore crucial for max- imum application of climate information and predic- tion services in agricultural planning and operations. Such records are not available in many countries. Even when they are available, locations, exposure and types of instruments may have changed. Changes have also occurred in observation routines, types of sensors, etc due to the fast development in science and technology. Such changes can make observations taken before and after such changes not too strictly comparable. These can mask the typical linkages be- tween climate and agricultural systems. The length and quality of the climate and agricultural records are key issues that must be addressed as they provide the information base in any efforts to optimize appli- cations of climate prediction products in agricultural planning and management in the next century. This requires user specific computerised databases, and improved agrometeorological networks. 3.2. Real and near real time climate information Some agricultural operations require real-time in- formation. Land use activities, however, vary from one location to another. The network of the agrome- teorological observations must be able to adequately represent large variations in the needs of the users. Real-time and near real-time users require timely availability of the agrometeorological information on a daily or weekly10-day basis. Information dissemi- nation and the network of agrometeorological stations are very poor in many regions, especially in the de- veloping countries. The locations of many weather stations rarely follow the pattern of agro-ecological zones and major areas of food production Gommes et al., 1996. Even in developed countries many weather stations are located near towns and cities, and at airports where exposure is not representative of the agricultural region. Recently, attempts have been made to enhance the use of remote sensing techniques to provide real and near real-time agrome- teorological information. Calibration of such methods for user specific applications has not been carried out in many regions, especially in many developing countries. In the early warning systems operated by FAO, agrometeorology and remote sensing already play an important role in monitoring and forecasting in a qualitative manner Gommes et al., 1996. Applications of remote sensing techniques hold the key to many future applications, but they will not be able, in the near future, to provide specific agromete- orological information at the farm level. Neither will they able to replace traditional farm level agrometeo- rological measurements in the near future. At present the advantage to be gained from remote sensing is pri- marily in the complete areal coverage of information it provides. The data derived from current satellites are qualitative or at best semi-quantitative indices re- lating to the dominant crop on a pixel or multi-pixel basis. In the longer term, advances in next generation satellite sensors will enable finer resolution and more quantitative detail to be provided at the local scale. 3.3. Short and medium-term weather forecasts Some agricultural systems and operations require not only daily climate records, but also cumulative weather information over several days or weeks. The 162 L.A. Ogallo et al. Agricultural and Forest Meteorology 103 2000 159–166 science and technology of short and medium range weather forecasting with computer models of the global climate processes, are now quite advanced. Operational short to medium range weather forecast products are now available in many climate centres world-wide. For such products to be useful in agricultural appli- cations, they have to be downscaled, not only to na- tional and regional levels, and ultimately to specific farm levels. Many downscaling techniques have been developed in recent years. These have ranged from the use of complex downscaling statistical methods, to the nesting of a meso-scale models within a general circu- lation model of the global climate system Von Storch et al., 1993; Hughes and Guttorp, 1994; Zorita et al., 1995; Kidson and Thomson, 1998 Although most of the short and medium-range weather forecast products are readily available to all National Meteorological and Hydrological Services NMHSs world-wide through the efforts of WMO and other institutional linkages, many developing countries still do not have adequate capacity to make use of the available products through further adapta- tion and post processing. Furthermore, most of the regionallocal scale down- scaling techniques require good knowledge of the localregional climate processes which are still lack- ing in many developing countries due to the serious limitations of the basic local meteorological research. The downscaling to specific farm level appears to be unrealistic in the foreseeable future as most of the techniques will at best only incorporate generalised regional characteristics. 3.4. Seasonal to interannual climate prediction The science and technology of climate prediction within monthly, seasonal to interannual time scales is still very young, and is currently under intensive investigation world-wide. The last 10 years, however, have witnessed a major advance in understanding the predictability of the atmosphere at seasonal to inter-annual time-scale Palmer and Anderson, 1993; NRC, 1996; Carson, 1998. The major impetus in cur- rent seasonal to interannual time scale prediction ef- forts was provided by the Tropical Ocean and Global Atmosphere TOGA Programme, which was carried out by WMO, ICSU and several other co-operating institutions between 1985–1994. Results from TOGA demonstrated that it is possible to predict Pacific ocean El Niño and Southern Oscillation ENSO related Sea Surface Temperatures SST over time scales extending from a few months to over 1 year. El Niño and Southern Oscillation, which are col- lectively known as ENSO, are some of the known key drivers to interannual variability, and have been associated with world-wide extreme climate anoma- lies, including changes in the space-time patterns of floods, droughts, cyclonesevere storms activity, coldhot spells, etc. Rasmusson and Wallace, 1983; Cane et al., 1986; Ogallo, 1988; Ropelewski and Halpert, 1989; NRC, 1996. The ability to forecast some aspects of ENSO signals for time scales of months to over one year are currently being used to extrapolate the potential occurrences ENSO related extreme weatherclimate events for specific seasons and regions of the world which have strong ENSO signals. Such information now forms crucial components of early warning sys- tems, including the planning, management and oper- ations of agricultural activities in some parts of the tropical regions. For some of these agricultural ap- plications have developed models which transfer pro- jected ENSO signals directly into agricultural stress indices Nicholls, 1985; Cane et al., 1994; Glantz, 1994; Keplinger and Mjelde, 1995; Hammer et al., 1996; Mjelde, 1998. While the association with ENSO signals are known to be strong in interannual climate variability over parts of US and many parts of the tropics, empirical studies have shown weak ENSO climate linkages over Europe. However, the recent rate of fast development in computer hardwaresoftware, together with the gen- eral advancement in communication and climate pre- diction science and technology seem to suggest that useful model-based seasonal forecasts are possible in future years. When strong perturbations are present, the possibility of a model signal being real is relatively high even in middle latitudes. It should, however, be noted that even in regions with strong ENSO signals, not all inter annual climate anomalies are caused by ENSO. Neither are all ENSOs similar in terms of their associated space-time climate anomalies. Improvement in the seasonal to interannual climate prediction is one crucial factor that could reduce the vulnerability of the agricultural systems to severe L.A. Ogallo et al. Agricultural and Forest Meteorology 103 2000 159–166 163 impacts of extreme interannual climate anomalies. The challenge to improve climate predictions for seasonal to interannual scales has been taken by the new WMOICSU programme known as the Study of Climate Variability and Predictability CLIVAR e.g. WMO, 1997a and in the implementation of the project on Climate Information and Predictions Ser- vices CLIPS e.g. WMO, 1997b. It is also a priority research area in many climate centres world-wide. 3.5. Climate change information It was noted in Section 1 that climate determines natural adaptation of ecosystems and specific land use activities at any given location. Any change in the traditional patterns of climate resulting in significant changes in the space-time patterns of climate parame- ters can have far reaching implications in agriculture. Such changes may be reflected in the mean rainfall patterns or changes in the frequency, intensity, dura- tion, seasonal distribution, year to year persistence of the extreme climate events such as droughts, floods, cyclones. Climate change is one of the current key scien- tific and policy issues which are of great concern to mankind due to the recent scientific studies which have confirmed that human activities can interfere and change the traditional patterns of the global climate systems which drive most of the global agricultural systems. Such human activities include agricultural practices, deforestation, desertification, overgrazing, intensive cultivation resulting in erosion, and many other activities of mankind arising from the fast in- creasing population IPCC, 1995b. The science of, climate change, its impacts, and the optimum adaptation and response strategies are currently being addressed by the UN wide system through the WMOUNEP Co-ordinated Intergovern- mental Panel on Climate Change IPCC. Several cli- mate change assessments have been provided since the establishment of IPCC in 1988 IPCC, 1995a, b, c. An effort has been initiated to provide another global cli- mate change assessment by the beginning of the next century. The IPCC assessments have played crucial roles in the recent climate change debates, including the negotiations of the three recent UN environmen- tal related conventions namely, conventions on climate change, desertification and biodiversity. The provision of climate change information, useful to those man- aging agricultural systems will be a key challenge of the next century. Realistic localregional climate change scenarios, required for studies of agricultural impacts and adap- tation assessments are still not available due to the current limitations of the available prediction science and technology. However, now available are some techniques which can be used to assess the impacts of particular agricultural practices under a fixed set of climate change scenarios IPCC, 1995a, b, c.

4. Challenges to optimum utilization of climate information and prediction products in